{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This is an example of how to use the Rameau function of Spectra.jl on a small portion of the dataset published in 2012 in \n",
    "\n",
    "Le Losq, Neuville, Moretti, Roux, 2012. Determination of water content in silicate glasses using Raman spectrometry: Implications for the study of explosive volcanism. American Mineralogist 97, 779-790.\n",
    "\n",
    "Please refer to the documentation available at http://spectrajl.readthedocs.io/en/latest/\n",
    "\n",
    "You must have a file liste and different folders already prepared before calling Rameau. Ensure this is done, follow the present example architecture and the fileliste example provided. Also consult the docs for further details, and if help is needed, please send an email!\n",
    "\n",
    "Please note that the present calculation are not identical to those in the 2012 paper, and are only for illustrative purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Recompiling stale cache file /Users/charles/.julia/lib/v0.5/Spectra.ji for module Spectra.\n"
     ]
    }
   ],
   "source": [
    "# we first call Spectra\n",
    "using Spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514.532"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we define the different paths (relative paths are used, i.,e. relative to where your code is placed)\n",
    "in_liste = \"./dataliste_rameau_2012.csv\" # the fileliste\n",
    "in_path = \"./raw_2012/\" # the repertory of raw data\n",
    "out_path = \"./treated/\" # where I want my treated data to be saved\n",
    "fig_path=\"./figures/\" #where I want the figures to be saved\n",
    "rws_save_file = \"rws_calculated.csv\" # the file with the rws\n",
    "rws_save_fig = \"rws_calculated.jpg\" # the calibration figure\n",
    "paths = (in_liste,in_path,out_path,fig_path,rws_save_file,rws_save_fig) # we put everything in a Tuple\n",
    "\n",
    "input_properties = ('\\t',0) # delimiter, number of lines to skip in my input data files\n",
    "\n",
    "# Mode (internal or external)? calibration (yes or no)? Experimental mode (double or no)? Long correction (yes or no)?\n",
    "switches = (\"internal\",\"yes\", \"double\", \"yes\") \n",
    "\n",
    "used_laser = 514.532 # nm, warning this is an additional that is currently set at 532 nm, so we need to change it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0111.txt, the rws is 6.095523668771038\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0112.txt, the rws is 16.918845343160736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0108.txt, the rws is 10.435808503274592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0107.txt, the rws is 2.0293255945361994\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ac8041.txt, the rws is 6.230359429873725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ac8015.txt, the rws is 1.2921018529694224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0192.txt, the rws is 3.6878593082552635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0674.txt, the rws is 0.2649623475014972\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0105.txt, the rws is 8.076212935842435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0087.txt, the rws is 3.556951810252207\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0120.txt, the rws is 4.771914597793912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0091.txt, the rws is 2.242352829013745\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0441.txt, the rws is 4.780497179129244\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0442.txt, the rws is 2.0238538787138234\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0443.txt, the rws is 2.1991890134966368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ae0664.txt, the rws is 5.567999033476766\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x32799b350>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum ad0110.txt, the rws is 3.2160492659899536\n"
     ]
    }
   ],
   "source": [
    "rameau(paths,switches,input_properties = ('\\t',0),laser = used_laser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Et voilà! \n",
    "This is it! You can look at the results in the different folders, and tweak the parameters in your liste file."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.5.0",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
